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通过整合环境因素和可解释的支向量机器模型,对LNAPL污染物度在剂区的定量表征

Rui Zuo¹, Zhiwen Wang¹, Yi Cai²

¹College of Water Science, Beijing Normal University, Beijing 100875, China; Engineering Research Center of Groundwater Pollution Control and Remediation, Ministry of Education, Beijing 100875, China.

Ecotoxicology and environmental safety

|March 6, 2026

概括

准确监测土壤中的轻非水相液体 (LNAPLs) 对地下水保护至关重要. 这项研究开发了一种使用环境因素和机器学习的新方法,以精确量化LNAPL污染并预测泄漏动态.

科学领域:

环境科学环境科学
地质化学地质化学
水文地质学水文地质学

背景情况:

准确监测轻非水相液体 (LNAPLs) 在剂区对于保护地下水资源至关重要.
柴油被用作代表性的LNAPL污染物来研究其运输行为.

研究的目的:

分析环境因素 (体积含水量,电导率,pH,ORP) 与LNAPL污染物运输之间的关系.
开发一种新的,可解释的方法,使用多环境因素和机器学习量化LNAPL度.

主要方法:

进行了沙实验,以收集LNAPL运输和环境因素的高精度数据.
一个支持向量机 (SVM) 算法与夏普利添加式扩展 (SHAP) 集成,用于模型构建.
在细砂剂区确定了三个不同的LNAPL运输阶段.

主要成果:

开发的模型表现出了优异的概括性能 (R2 > 0.90),用于量化LNAPL污染羽毛和预测泄漏.
SHAP分析发现体积含水量 (θ) 是预测石油总碳化合物 (TPH) 度的最有影响的因素.
在影响TPH度及其贡献的环境因素中显著的空间异质性被定量地揭示出来.

结论:

这项研究为实时评估土壤LNAPL污染风险提供了一个强大的,可解释的模型.
这些发现支持开发基于环境因素的LNAPL泄漏监测和管理策略.
这项研究为保护地下水免受LNAPL污染提供了理论和建模见解.

关键词:

环境因素环境因素轻度非水相液体轻度非水相液体量化表征量化表征量化表征巧妙的添加式扩展.支持矢量机器的支持矢量机器.瓦多塞区是瓦多塞地区.

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